Echo State Networks as Novel Approach for Low-Cost Myoelectric Control

Published on Jun 21, 2017 in AIME (Artificial Intelligence in Medicine in Europe)
· DOI :10.1007/978-3-319-59758-4_40
Cosima Prahm5
Estimated H-index: 5
(Medical University of Vienna),
Alexander Schulz38
Estimated H-index: 38
(Bielefeld University)
+ 3 AuthorsGeorg Dorffner27
Estimated H-index: 27
(Medical University of Vienna)
Myoelectric signals (EMG) provide an intuitive and rapid interface for controlling technical devices, in particular bionic arm prostheses. However, inferring the intended movement from a surface EMG recording is a non-trivial pattern recognition task, especially if the data stems from low-cost sensors. At the same time, overly complex models are prohibited by strict speed, data parsimony and robustness requirements. As a compromise between high accuracy and strict requirements we propose to apply Echo State Networks (ESNs), which extend standard linear regression with (1) a memory and (2) nonlinearity. Results show that both features, memory and nonlinearity, independently as well as in conjunction, improve the prediction accuracy on simultaneous movements in two degrees of freedom (hand opening/closing and pronation/supination) recorded from four able-bodied participants using a low-cost 8-electrode-array. However, it was also shown that the model is still not sufficiently resistant to external disturbances such as electrode shift.
  • References (19)
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Mar 1, 2015 in VR (IEEE Virtual Reality Conference)
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#1Dario Farina (GAU: University of Göttingen)H-Index: 76
#2Ning Jiang (GAU: University of Göttingen)H-Index: 29
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Jan 1, 2014 in NeurIPS (Neural Information Processing Systems)
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Cited By2
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